Kaiming Liu

A More Efficient Diffusing Update Algorithm For Loop-Free Routing

Enchanced Interior Gate Routing Protocol(EIGRP) is a kind of distance vector routing protocol developed by Cisco, and it is mainly based on Diffusing Update Algorithm(DUAL) which computes the best path distributedly without resulting in routing-touble loops and counting-to-infinite problem. DUAL converges as fast as OSPF but leads to less overhead. In this paper, a more efficient diffusing update algorithm is presented and proved to create no loops. In this algorithm, the count of diffusing computation is reduced through two revised feasible conditions. The result of experiment shows that our algorithm could converge faster than the original DUAL.

Multiband Detection for Spectrum Sensing: A Multistage Wiener Filter Perspective

In the recent years, spectrum scarcity becomes an urgent issue due to the emergence of wireless services. The effective utilization of spectrum white space has gained significant research interests. Cognitive radio techniques have been paid much attention to the television white space. This paper raises a multiband spectrum sensing scheme to detect the spectrum white space which is not limited to television bands. When performing spectrum sensing, our approach operates over the total frequency bands simultaneously rather than a single band each time. By applying the idea of multistage Wiener filter to Gerschgorin disk estimator, our approach jointly makes the decision. In this way, the proposed method is able to capture the signal information and suppress the additive noise, which brings about an enhanced detection performance. Distinct from the classical methods, the proposed scheme requires neither noise power estimation nor prior knowledge of primary user signal, thereby being robust to noise uncertainty and suitable for blind detection. On the contrary, in the context of noise uncertainty, noise variance has no access to accurate estimation, inducing an imprecise decision threshold, which severely deteriorates the detection performance. Besides, our method avoids the estimation of covariance matrix as well as eigenvalue decomposition, and thus achieves a low computational complexity. This paper presents simulations under various conditions to verify the performance of the proposed scheme and the results show that it is superior to the existing sensing algorithms.

Throughput oriented forwarders selection analysis for opportunistic routing in wireless mesh network

Abstract Opportunistic routing explicitly takes advantage of the broadcast nature of wireless communications by using a set of forwarders to opportunistically perform packet forwarding. A key issue in the design of opportunistic routing protocols is the forwarder list selection problem. This paper proposes a novel routing metric which shows the end-to-end throughput and a corresponding throughput oriented opportunistic routing forwarder-selecting algorithm throughput oriented forwarders selection (TOFS) through analyzing forwarding characteristics of forwarders. The algorithm puts forward a constraint mechanism that controls the number of forwarders by constraint of throughput for forwarders selection, achieving a better balance between number of forwarders and effective link stability by introducing the factor of transmission time. Simulation results show that the algorithm can improve the network end-to-end throughput effectively over existing methods.

Relay joint antenna mode selection with power allocation in cognitive relay network

The article proposes a kind of joint relay and antenna selection scheme with the optimal power allocation in full-duplex(FD) cognitive relay network(CRN), including a pair of source and destination node, some FD amplify-and-forward (AF) relays and one primary user. In this work, the transmit (Tx) antenna and receive (Rx) antenna of each relay are selected adaptively. Then, the relay under optimal power allocation with optimal antenna mode is chosen to enhance the system performance. The closed-form expressions of the optimal power allocation are derived. And the performance of the proposed scheme is analyzed. Numerical results demonstrate that the system performance can be greatly improved through employing the ORAMS-PA, especially when the loop-interference is low. It is shown that the proposed scheme is realizable and significant in CRN with respect to improve spectral efficiency.

Fairness-oriented routing algorithm joint with power control and channel assignment for multi-radio multi-channel wireless mesh networks

Abstract The multi-radio multi-channel wireless mesh network (MRMC-WMN) draws general attention because of its excellent throughput performance, robustness and relative low cost. The closed interactions among power control (PC), channel assignment (CA) and routing is contributed to the performance of multi-radio multi-channel wireless mesh networks (MRMC-WMNs). However, the joint PC, CA and routing (JPCR) design, desired to achieve a global optimization, was poor addressed. The authors present a routing algorithm joint with PC and CA (JPCRA) to seek the routing, power and channel scheme for each flow, which can improve the fairness performance. Firstly, considering available channels and power levels, the routing metric, called minimum flow rate, is designed based on the physical interference and Shannon channel models. The JPCRA is presented based on the genetic algorithm (GA) with simulated annealing to maximize the minimum flow rate, an non-deterministic polynomial-time hard (NP-Hard) problem. Simulations show the JPCRA obtains better fairness among different flows and higher network throughput.

An exploratory research of elitist probability schema and its applications in evolutionary algorithms

An important problem in the study of evolutionary algorithms is how to continuously predict promising solutions while simultaneously escaping from local optima. In this paper, we propose an elitist probability schema (EPS) for the first time, to the best of our knowledge. Our schema is an index of binary strings that expresses the similarity of an elitist population at every string position. EPS expresses the accumulative effect of fitness selection with respect to the coding similarity of the population. For each generation, EPS can quantify the coding similarity of the population objectively and quickly. One of our key innovations is that EPS can continuously predict promising solutions while simultaneously escaping from local optima in most cases. To demonstrate the abilities of the EPS, we designed an elitist probability schema genetic algorithm and an elitist probability schema compact genetic algorithm. These algorithms are estimations of distribution algorithms (EDAs). We provided a fair comparison with the persistent elitist compact genetic algorithm (PeCGA), quantum-inspired evolutionary algorithm (QEA), and particle swarm optimization (PSO) for the 0–1 knapsack problem. The proposed algorithms converged quicker than PeCGA, QEA, and PSO, especially for the large knapsack problem. Furthermore, the computation time of the proposed algorithms was less than some EDAs that are based on building explicit probability models, and was approximately the same as QEA and PSO. This is acceptable for evolutionary algorithms, and satisfactory for EDAs. The proposed algorithms are successful with respect to convergence performance and computation time, which implies that EPS is satisfactory.

The Influence Analysis of Pseudorandom Number Generators and Low Discrepancy Sequences for the Family of Compact Genetic Algorithms: Search Behavior Research from Outside Causes to Internal Causes

According to probability vectors, compact genetic algorithms (CGAs) generate new individuals by using pseudorandom number generators (PRNGs) or low discrepancy sequences (LDSs). These new generated individuals are the only factors which determine the search directions in CGAs. Therefore, we experimentally study the relationship between probability vectors and PRNGs (or LDSs). Moreover, we primarily investigate the influence analysis of PRNGs and LDSs for the effectiveness and efficiency of the family of CGAs by using analysis of variance (ANOVA), success rate and success performance. According to experimental results, we provide conclusive evidence to suggest using PRNGs (or LDSs) for CGAs. In essence, the frameworks of CGAs and the update method of probability vectors of CGAs are the internal causes that determine the performance of CGAs for different PRNGs and LDSs.

Degrees of freedom for MIMO interference broadcast channels

Abstract Multiple-input multiple-output (MIMO) interference broadcast channel (IBC) plays an important role in the modern wireless communications. The upper bound of degrees of freedom (DoF) and its Corresponding achievable schemes was investigated. However, all the achievable schemes require perfect channel state information at transmitters (CSIT). In absence of CSIT, the DoF value is still unknown. This article mainly focuses on the G-cell K-user MIMO IBC, where there are M antennas at each transmitter and N antennas at each receiver. The transmitters only know channel coherence time internals rather than the values of channel coefficients. The users in the same cell are assumed to be able to share the channel information. Based on a heterogeneous semi-staggered block fading model, a blind interference alignment (IA) scheme was proposed for this scenario. It is shown that when R≥1/(G-1) and R = min(M,KN)/max(M,KN), a total of GKMN/(M + KN) DoF can be achieved. The inner bound is same with the decomposition DoF upper bound. Since the complexity is an important performance index to evaluate the achievable scheme, the quantitative analysis for the complexity is presented.

Optimum Power Loading with Arbitrary Square MQAM for OFDM Systems

An adaptive power loading algorithm for orthogonal frequency division multiplexing (OFDM) systems with given arbitrary square M-ary quadrature amplitude modulation (MQAM) modes on subcarriers is proposed in this paper. This algorithm aims to minimize the total transmit power while guaranteeing the target overall bit error rate (BER), and can achieve the optimum power loading results through optimizing the BER distribution. The closed-form expressions for optimal BER and power distributions are presented. Simulation results indicate the superiority of the proposed algorithm in terms of algorithmic performance and complexity.